Update pyproject.toml (#659)

aixplain/factories/agent_factory/__init__.py

@@ -98,10 +98,10 @@ def create(
         from aixplain.utils.llm_utils import get_llm_instance
 
         if llm is None and llm_id is not None:
-            llm = get_llm_instance(llm_id, api_key=api_key)
+            llm = get_llm_instance(llm_id, api_key=api_key, use_cache=True)
         elif llm is None:
             # Use default GPT-4o if no LLM specified
-            llm = get_llm_instance("669a63646eb56306647e1091", api_key=api_key)
+            llm = get_llm_instance("669a63646eb56306647e1091", api_key=api_key, use_cache=True)
 
         if output_format == OutputFormat.JSON:
             assert expected_output is not None and (
@@ -152,7 +152,7 @@ def create(
         }
 
         if llm is not None:
-            llm = get_llm_instance(llm, api_key=api_key) if isinstance(llm, str) else llm
+            llm = get_llm_instance(llm, api_key=api_key, use_cache=True) if isinstance(llm, str) else llm
             payload["tools"].append(
                 {
                     "type": "llm",
@@ -519,11 +519,12 @@ def list(cls) -> Dict:
             raise Exception(error_msg)
 
     @classmethod
-    def get(cls, agent_id: Text, api_key: Optional[Text] = None) -> Agent:
-        """Retrieve an agent by its ID.
+    def get(cls, agent_id: Optional[Text] = None, name: Optional[Text] = None, api_key: Optional[Text] = None) -> Agent:
+        """Retrieve an agent by its ID or name.
 
         Args:
-            agent_id (Text): ID of the agent to retrieve.
+            agent_id (Optional[Text], optional): ID of the agent to retrieve.
+            name (Optional[Text], optional): Name of the agent to retrieve.
             api_key (Optional[Text], optional): API key for authentication.
                 Defaults to None, using the configured TEAM_API_KEY.
 
@@ -532,14 +533,23 @@ def get(cls, agent_id: Text, api_key: Optional[Text] = None) -> Agent:
 
         Raises:
             Exception: If the agent cannot be retrieved or doesn't exist.
+            ValueError: If neither agent_id nor name is provided, or if both are provided.
         """
         from aixplain.factories.agent_factory.utils import build_agent
 
-        url = urljoin(config.BACKEND_URL, f"sdk/agents/{agent_id}")
+        # Validate that exactly one parameter is provided
+        if not (agent_id or name) or (agent_id and name):
+            raise ValueError("Must provide exactly one of 'agent_id' or 'name'")
+
+        # Construct URL based on parameter type
+        if agent_id:
+            url = urljoin(config.BACKEND_URL, f"sdk/agents/{agent_id}")
+        else:  # name is provided
+            url = urljoin(config.BACKEND_URL, f"sdk/agents/by-name/{name}")
 
         api_key = api_key if api_key is not None else config.TEAM_API_KEY
         headers = {"x-api-key": api_key, "Content-Type": "application/json"}
-        logging.info(f"Start service for GET Agent  - {url} - {headers}")
+        logging.info(f"Start service for GET Agent - {url} - {headers}")
         r = _request_with_retry("get", url, headers=headers)
         resp = r.json()
         if 200 <= r.status_code < 300:

aixplain/factories/agent_factory/utils.py

@@ -140,7 +140,7 @@ def build_llm(payload: Dict, api_key: Text = config.TEAM_API_KEY) -> LLM:
         for tool in payload["tools"]:
             if tool["type"] == "llm" and tool["description"] == "main":
 
-                llm = get_llm_instance(payload["llmId"], api_key=api_key)
+                llm = get_llm_instance(payload["llmId"], api_key=api_key, use_cache=True)
                 # Set parameters from the tool
                 if "parameters" in tool:
                     # Apply all parameters directly to the LLM properties
@@ -191,18 +191,34 @@ def build_agent(payload: Dict, tools: List[Tool] = None, api_key: Text = config.
     payload_tools = tools
     if payload_tools is None:
         payload_tools = []
-        for tool in tools_dict:
+        # Use parallel tool building with ThreadPoolExecutor for better performance
+        from concurrent.futures import ThreadPoolExecutor, as_completed
+
+        def build_tool_safe(tool_data):
+            """Build a single tool with error handling"""
             try:
-                payload_tools.append(build_tool(tool))
+                return build_tool(tool_data)
             except (ValueError, AssertionError) as e:
                 logging.warning(str(e))
-                continue
+                return None
             except Exception:
                 logging.warning(
-                    f"Tool {tool['assetId']} is not available. Make sure it exists or you have access to it. "
+                    f"Tool {tool_data['assetId']} is not available. Make sure it exists or you have access to it. "
                     "If you think this is an error, please contact the administrators."
                 )
-                continue
+                return None
+
+        # Build all tools in parallel (only if there are tools to build)
+        if len(tools_dict) > 0:
+            with ThreadPoolExecutor(max_workers=min(len(tools_dict), 10)) as executor:
+                # Submit all tool build tasks
+                future_to_tool = {executor.submit(build_tool_safe, tool): tool for tool in tools_dict}
+
+                # Collect results as they complete
+                for future in as_completed(future_to_tool):
+                    tool_result = future.result()
+                    if tool_result is not None:
+                        payload_tools.append(tool_result)
 
     llm = build_llm(payload, api_key)
 

aixplain/factories/team_agent_factory/__init__.py

@@ -334,14 +334,15 @@ def list(cls) -> Dict:
             raise Exception(error_msg)
 
     @classmethod
-    def get(cls, agent_id: Text, api_key: Optional[Text] = None) -> TeamAgent:
-        """Retrieve a team agent by its ID.
+    def get(cls, agent_id: Optional[Text] = None, name: Optional[Text] = None, api_key: Optional[Text] = None) -> TeamAgent:
+        """Retrieve a team agent by its ID or name.
 
         This method fetches a specific team agent from the platform using its
-        unique identifier.
+        unique identifier or name.
 
         Args:
-            agent_id (Text): Unique identifier of the team agent to retrieve.
+            agent_id (Optional[Text], optional): Unique identifier of the team agent to retrieve.
+            name (Optional[Text], optional): Name of the team agent to retrieve.
             api_key (Optional[Text], optional): API key for authentication.
                 Defaults to None, using the configured TEAM_API_KEY.
 
@@ -350,15 +351,25 @@ def get(cls, agent_id: Text, api_key: Optional[Text] = None) -> TeamAgent:
 
         Raises:
             Exception: If:
-                - Team agent ID is invalid
+                - Team agent ID/name is invalid
                 - Authentication fails
                 - Service is unavailable
                 - Other API errors occur
+            ValueError: If neither agent_id nor name is provided, or if both are provided.
         """
-        url = urljoin(config.BACKEND_URL, f"sdk/agent-communities/{agent_id}")
+        # Validate that exactly one parameter is provided
+        if not (agent_id or name) or (agent_id and name):
+            raise ValueError("Must provide exactly one of 'agent_id' or 'name'")
+
+        # Construct URL based on parameter type
+        if agent_id:
+            url = urljoin(config.BACKEND_URL, f"sdk/agent-communities/{agent_id}")
+        else:  # name is provided
+            url = urljoin(config.BACKEND_URL, f"sdk/agent-communities/by-name/{name}")
+
         api_key = api_key if api_key is not None else config.TEAM_API_KEY
         headers = {"x-api-key": api_key, "Content-Type": "application/json"}
-        logging.info(f"Start service for GET Team Agent  - {url} - {headers}")
+        logging.info(f"Start service for GET Team Agent - {url} - {headers}")
         try:
             r = _request_with_retry("get", url, headers=headers)
             resp = r.json()

aixplain/factories/team_agent_factory/utils.py

@@ -59,15 +59,31 @@ def build_team_agent(payload: Dict, agents: List[Agent] = None, api_key: Text =
     payload_agents = agents
     if payload_agents is None:
         payload_agents = []
-        for i, agent in enumerate(agents_dict):
+        # Use parallel agent fetching with ThreadPoolExecutor for better performance
+        from concurrent.futures import ThreadPoolExecutor, as_completed
+
+        def fetch_agent(agent_data):
+            """Fetch a single agent by ID with error handling"""
             try:
-                payload_agents.append(AgentFactory.get(agent["assetId"]))
-            except Exception:
+                return AgentFactory.get(agent_data["assetId"])
+            except Exception as e:
                 logging.warning(
-                    f"Agent {agent['assetId']} not found. Make sure it exists or you have access to it. "
-                    "If you think this is an error, please contact the administrators."
+                    f"Agent {agent_data['assetId']} not found. Make sure it exists or you have access to it. "
+                    "If you think this is an error, please contact the administrators. Error: {e}"
                 )
-                continue
+                return None
+
+        # Fetch all agents in parallel (only if there are agents to fetch)
+        if len(agents_dict) > 0:
+            with ThreadPoolExecutor(max_workers=min(len(agents_dict), 10)) as executor:
+                # Submit all agent fetch tasks
+                future_to_agent = {executor.submit(fetch_agent, agent): agent for agent in agents_dict}
+
+                # Collect results as they complete
+                for future in as_completed(future_to_agent):
+                    agent_result = future.result()
+                    if agent_result is not None:
+                        payload_agents.append(agent_result)
 
     # Ensure custom classes are instantiated: for compatibility with backend return format
     inspectors = []
@@ -90,6 +106,15 @@ def build_team_agent(payload: Dict, agents: List[Agent] = None, api_key: Text =
     # Get LLMs from tools if present
     supervisor_llm = None
     mentalist_llm = None
+
+    # Cache for models to avoid duplicate fetching of the same model ID
+    model_cache = {}
+
+    def get_cached_model(model_id: str) -> any:
+        """Get model from cache or fetch if not cached"""
+        if model_id not in model_cache:
+            model_cache[model_id] = ModelFactory.get(model_id, api_key=api_key, use_cache=True)
+        return model_cache[model_id]
 
     # First check if we have direct LLM objects in the payload
     if "supervisor_llm" in payload:
@@ -100,14 +125,8 @@ def build_team_agent(payload: Dict, agents: List[Agent] = None, api_key: Text =
     elif "tools" in payload:
         for tool in payload["tools"]:
             if tool["type"] == "llm":
-                try:
-                    llm = ModelFactory.get(payload["llmId"], api_key=api_key)
-                except Exception:
-                    logging.warning(
-                        f"LLM {payload['llmId']} not found. Make sure it exists or you have access to it. "
-                        "If you think this is an error, please contact the administrators."
-                    )
-                    continue
+                # Use cached model fetching to avoid duplicate API calls
+                llm = get_cached_model(payload["llmId"])
                 # Set parameters from the tool
                 if "parameters" in tool:
                     # Apply all parameters directly to the LLM properties
@@ -258,7 +277,7 @@ def build_team_agent_from_yaml(yaml_code: str, llm_id: str, api_key: str, team_i
     team_name = system_data.get("name", "")
     team_description = system_data.get("description", "")
     team_instructions = system_data.get("instructions", "")
-    llm = ModelFactory.get(llm_id)
+    llm = ModelFactory.get(llm_id, use_cache=True)
     # Create agent mapping by name for easier task assignment
     agents_mapping = {}
     agent_objs = []

aixplain/modules/agent/__init__.py

@@ -175,7 +175,7 @@ def _validate(self) -> None:
             re.match(r"^[a-zA-Z0-9 \-\(\)]*$", self.name) is not None
         ), "Agent Creation Error: Agent name contains invalid characters. Only alphanumeric characters, spaces, hyphens, and brackets are allowed."
 
-        llm = get_llm_instance(self.llm_id, api_key=self.api_key)
+        llm = get_llm_instance(self.llm_id, api_key=self.api_key, use_cache=True)
 
         assert llm.function == Function.TEXT_GENERATION, "Large Language Model must be a text generation model."
 

aixplain/modules/agent/tool/model_tool.py

@@ -247,7 +247,7 @@ def _get_model(self, model_id: Text = None):
         from aixplain.factories.model_factory import ModelFactory
 
         model_id = model_id or self.model
-        return ModelFactory.get(model_id, api_key=self.api_key)
+        return ModelFactory.get(model_id, api_key=self.api_key, use_cache=True)
 
     def validate_parameters(self, received_parameters: Optional[List[Dict]] = None) -> Optional[List[Dict]]:
         """Validates and formats the parameters for the tool.

aixplain/modules/team_agent/__init__.py

@@ -693,7 +693,7 @@ def _validate(self) -> None:
         ), "Team Agent Creation Error: Team name contains invalid characters. Only alphanumeric characters, spaces, hyphens, and brackets are allowed."
 
         try:
-            llm = get_llm_instance(self.llm_id)
+            llm = get_llm_instance(self.llm_id, use_cache=True)
             assert llm.function == Function.TEXT_GENERATION, "Large Language Model must be a text generation model."
         except Exception:
             raise Exception(f"Large Language Model with ID '{self.llm_id}' not found.")

aixplain/utils/llm_utils.py

@@ -6,12 +6,14 @@
 def get_llm_instance(
     llm_id: Text,
     api_key: Optional[Text] = None,
+    use_cache: bool = True,
 ) -> LLM:
     """Get an LLM instance with specific configuration.
 
     Args:
         llm_id (Text): ID of the LLM model to use.
         api_key (Optional[Text], optional): API key to use. Defaults to None.
+        use_cache (bool, optional): Whether to use caching for model retrieval. Defaults to True.
 
     Returns:
         LLM: Configured LLM instance.
@@ -20,7 +22,7 @@ def get_llm_instance(
         Exception: If the LLM model with the given ID is not found.
     """
     try:
-        llm = ModelFactory.get(llm_id, api_key=api_key)
+        llm = ModelFactory.get(llm_id, api_key=api_key, use_cache=use_cache)
         return llm
     except Exception:
         raise Exception(f"Large Language Model with ID '{llm_id}' not found.")

aixplain/v2/agent.py

@@ -71,10 +71,10 @@ def list(cls, **kwargs: Unpack[BareListParams]) -> Page["Agent"]:
         return AgentFactory.list(**kwargs)
 
     @classmethod
-    def get(cls, id: str, **kwargs: Unpack[BareGetParams]) -> "Agent":
+    def get(cls, id: Optional[str] = None, name: Optional[str] = None, **kwargs: Unpack[BareGetParams]) -> "Agent":
         from aixplain.factories import AgentFactory
 
-        return AgentFactory.get(agent_id=id)
+        return AgentFactory.get(agent_id=id, name=name)
 
     @classmethod
     def create(cls, *args, **kwargs: Unpack[AgentCreateParams]) -> "Agent":